Our work is related to question understanding, and modular approaches to question answering that advocate decomposing questions into smaller ones  [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}. However, question decomposition in prior work is aimed at enhancing model performance, and is generally not human friendly. Notions of interpretability are provided through attention modules [1]}, or explicit meaning representations such as first-order logic [10]}, or custom semantic parsing formalisms [11]}, [8]}.
In contrast, our goal is to produce output that can be used by humans, and potentially by models too.
Additionally, questions in most prior work are short, containing just a single sentence. We target longer multi-sentence problems.
